Utility monitoring systems and methods of use

ABSTRACT

Various embodiments of the present disclosure provide methods, systems, and devices for monitoring one or more utilities consumed within a monitored area. At least certain disclosed methods include detecting an amount of a first utility consumed by a load associated with a device. An amount of a second utility consumed by a load associated with the device is detected. The identity of the device is determined based on the amount of the first and second utility consumed. In further disclosed methods, a utility monitoring method is disclosed that includes measuring an amount of a utility consumed by a first device at a first time and an amount of a utility consumed by a second device at a second time. The identity of the first device is determined based on the measured consumption of the first and second devices at the first and second time. In certain implementations, the electricity meter uses an effective variance analysis of the conductance waveform on an electrical circuit to identify specific appliances.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of, and incorporates by reference,U.S. Provisional Patent Application No. 60/803,323, filed May 26, 2006.

TECHNICAL FIELD

The present application relates generally to utility monitors and theirmethods of use. In particular examples, the present application providesa non-intrusive utility monitor which can be used to monitor a pluralityof discrete loads.

BACKGROUND

In most residences and small businesses, utility consumers learn abouttheir utility consumption through a bill delivered once per month. Thissystem has limited ability to promote conservation since the consumerdoes not have feedback to associate their specific activities to aparticular cost. This lack of information can result in a disconnectbetween the perception of utility costs and their actual costs. The lackof timely and/or specific information can also lead to inefficienciesand poor decision making by individuals. Although gasoline prices areoften a top concern for consumers, the average household spends morethan twice as much on utilities as on gasoline. In 2003, an averagehousehold spent $2811 on residential utilities, fuels, and publicservices; while spending $1333 on gasoline and motor oil.

Large commercial businesses commonly employ technology for energy andutility management. Typically a building engineer reviews utilityconsumption data to ensure utilities are consumed in a cost effectivemanner. This technology has not penetrated the small business andresidential market due to the system and installation expense. Moreover,the data provided by typical existing systems is not presented in a waythat non-technical users can understand. Existing monitoring systemsalso typically do not monitor multiple utilities (i.e. natural gas,electric power, and water), or correlate the use of different utilitiesto one another.

Many existing electricity monitoring devices attach sensors to eachelectrical appliance to be monitored. However, attaching sensors andtransmitters to numerous appliances in the areas to be monitored can addsubstantial cost to the system. Such a configuration can also increasethe complexity of the system and the difficulty of installing it.

Some existing systems monitor electric loads at the point of serviceentry into the areas to be monitored, such as an electrical meterinstalled by a power company. However, such systems typically onlyanalyze overall electricity use and do not analyze use by individualcircuits or appliances.

SUMMARY

The present disclosure provides apparatus, systems, and methods formonitoring one or more utilities. In a particular embodiment, thepresent disclosure provides a utility monitoring method that improves onthe above mentioned methods that are currently employed. An amount of afirst utility consumed by a load associated with a device is detected.An amount of a second utility consumed by a load associated with thesame device is detected. The identity of the device is determined basedon the amount of the first and second utilities consumed. In a specificexample, the first utility is electricity and the second utility is gas.In another example, the first utility is electricity and the secondutility is water. In a further example, the first utility is water andthe second utility is gas. In particular examples, electricity use isanalyzed using a non-intrusive load monitoring algorithm, such as aneffective variance analysis of the conductance waveform on an electricalcircuit.

According to a further disclosed utility monitoring method, an amount ofa utility consumed by a first device at a first time is measured. Anamount of a utility consumed by a second device at a second time ismeasured. The first device is identified based on the measuredconsumption of the first and second devices.

In various disclosed methods, the time of day of the use, the durationof use, or the separation of time between uses are used to help identifythe first device. In yet further methods sequence-based heuristics areused to identify which sequence of utility consumption is more probable.

The present disclosure also provides an energy monitoring system. In aparticular embodiment, the energy monitoring system includes anelectricity meter and another utility meter, such as a water meter ornatural gas meter; a signal processor; and a user interface device oruser computer in communication with the electricity meter and the otherutility meter. In certain implementations, the electricity meterincludes a non-intrusive load monitor algorithm, such as an effectivevariance analysis of the conductance waveform on an electrical circuit.In specific examples, the electricity meter is in communication witheach of a plurality of circuits. In such examples, the system need notinclude the water meter. Some implementations of the electricity meterinclude a signal processor.

In particular implementations that include an electricity meter incommunication with each of a plurality of circuits, the electricitymeter includes a power line modem or wireless transceiver. In furtherimplementations, the electricity meter has multiple monitoring channels,each channel being in communication with a current transducer or voltagesensor attached to a circuit of a monitored area. In one example, eachchannel is in communication with a circuit at a circuit box, or fusebox, servicing a monitored area.

Certain embodiments of the present disclosure include a user interfacedevice. The user interface device is in communication with one or moreutility meters. The user interface, in some examples, processes datafrom the utility meters, such as with a signal processor. In certainimplementations, the user interface device includes a user input deviceand a display. The display may be used to communicate utilityconsumption information to the user. The user input device may be usedby the user to select utility information to view, or to controloperation of the utility monitoring system.

In some embodiments of the disclosed systems, the user's computer is incommunication with the utility meters. The user computer may also beused to process data (such as with a signal processor), displayinformation to the user, or allow the user to control the monitoringsystem.

In further embodiments, the user interface device serves as an interfacebetween the utility meters and a user's computer. The user's computermay be used to display utility consumption information and otherinformation to the user. The user's computer may also be used by theuser to control the utility monitoring system. In yet furtherembodiments, the user's computer or remote system may communicate withthe user interface using a network protocol, such as TCP/IP.

In various embodiments, the user's computer or user interface device maybe in communication with a remote network, such as with or through theinternet. A remote network computer may store data from the utilitymeters, process such data, and transmit data to the user interfacedevice or user's computer. For example, the remote network computer maycontain profiles of various utility loads for comparison against theuser's monitored area, such as for load identification or benchmarking.In other embodiments, load profiles are stored on the user's computer,the user interface device, or the signal processor. The remote systemmay be used to transmit applicable advertisements or offers to the user,in some examples such offers originate from appliance vendors, serviceorganizations, or home improvement companies.

Additional embodiments provide methods for identifying an electricaldevice based on the electrical consumption of the device. One suchmethod includes measuring the voltage and current consumed by theappliance at a plurality of times. The conductance of the device isdetermined at each of the plurality of times. The device is identifiedbased on the change in conductance over time. In some implementations,the device is identified by comparing the change in conductance overtime to a reference value. In further implementations the change inconductance over time is analyzed using statistical analysis, such assubjecting the change in conductance over time to a least squaresanalysis, for example, an analysis of the effective variance of theconductance. In a specific example, the time over which the conductanceis measured is at least about one minute. In a further example, theconductance is measured once per voltage cycle.

The disclosed methods, systems, and apparatus can allow users to receivedata regarding utility consumption relatively concurrently with suchuse. The availability of such data can better aid users in adjustingtheir utility use. In a particular example, consumption data isconverted to an estimate of the environmental impacts of the utilityconsumption, such as an estimate of greenhouse gas emissions. Thepresent disclosure can also allow utility consumption to be more easily,conveniently, and accurately attributed to various sources ofconsumption. In particular methods, the present disclosure allows thethermal efficiency of a monitored area to be measured. Such measurementscan be used to help guide purchasing or renovation decisions for themonitored area.

There are additional features and advantages of the subject matterdescribed herein. They will become apparent as this specificationproceeds.

In this regard, it is to be understood that this is a brief summary ofvarying aspects of the subject matter described herein. The variousfeatures described in this section and below for various embodiments maybe used in combination or separately. Any particular embodiment need notprovide all features noted above, nor solve all problems or address allissues in the prior art noted above.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments are shown and described in connection with thefollowing drawings in which:

FIG. 1 is a schematic diagram of a disclosed utility monitoring system.

FIG. 2 is a schematic diagram of a particular implementation of theutility monitoring system of FIG. 1.

FIG. 3 is a schematic diagram of an implementation of the utilitymonitoring system of FIG. 1 capable of monitoring a plurality ofindividual electrical circuits.

FIG. 4 is a graph of the average conductance waveforms for fourappliances.

FIG. 5 is a graph of the average conductance waveforms of an airconditioner (2100 W) and a reading lamp (18 W).

FIG. 6 is a series of graphs showing chi-square calculations of theconductance waveforms of an air conditioner versus the air conditionerplus a reading lamp, and of an air conditioner versus itself.

FIG. 7 is a graph of chi-square values versus averaging time.

FIG. 8 is a graph of simulated utility data for an electricity meter, agas meter, and a water meter illustrating how loads can be identifiedbased on analyzing multiple utilities.

FIG. 9 is a graph of simulated utility data from an electricity meterand a water meter illustrating how sequence heuristics can be used toidentify loads.

FIG. 10 is a process diagram illustrating how data obtained from water,gas, and electricity meters may be deconvoluted to provide consumptioninformation for specific loads.

FIG. 11 is a sample user interface screen that may be displayed on auser's computer or the user interface by the utility monitoring systemof FIG. 1.

DETAILED DESCRIPTION

As used herein, the singular forms “a,” “an,” and “the” refer to one ormore than one, unless the context clearly dictates otherwise. As usedherein, the term “includes” means “comprises.” Unless the contextclearly indicates otherwise, the disjunctive “or” includes theconjunctive “and.”

Utility Monitoring Systems

FIG. 1 illustrates, generally, a disclosed utility monitoring system100. The utility monitoring system 100 includes a utility monitor 110 incommunication with various utility meters 120, 130, 140. As used herein,“meter” refers to a device that can track consumption or use of aparticular quantity. For example, an electrical meter that trackselectricity consumption can be a meter installed by a power company oranother metering device, such as a supplemental electrical meter (notshown) installed in a circuit breaker panel.

The utility monitor 110 can be, or include, a multiplexer, analog todigital converter, digital signal processor, or power line interfacedevice. In FIG. 1, the utility meters 120, 130, 140 are, respectively, awater meter, an electric meter, and a gas meter. However, the monitoringsystem 100 need not include all of the meters 120, 130, 140. Inparticular implementations, the monitoring system 100 includesadditional meters, such as, for example, a heating oil meter (notshown). The monitoring system 100 can include additional monitors orsensors, such as temperature sensors 145 or other ambient sensors (suchas humidity, or pressure sensors).

The utility monitor 110 is in communication with a user interface 150.The user interface 150 may optionally be in communication with a usercomputer 160. The user interface 150 can be connected to the usercomputer 160 by, for example, USB, serial, parallel, SCSI, RS232, SPI,I2C, Ethernet, wireless protocol, power line communications (such as theHomeplug standard), ZigBee, or PS/2 connections. In someimplementations, the user interface 150 is a stand alone component andneed not be connected to the user computer 160. In furtherimplementations, the user interface 150, or user computer 160, is incommunication with a remote server 170 over a network 180, such as theinternet. In some implementations, the utility monitor 110 is integratedinto the user interface 150 or the user computer 160. In yet furtherimplementations, the utility monitor 110 is omitted and the meters 120,130, 140 communicate directly with the user interface 150 or the usercomputer 160. Some embodiments omit the user interface 150. The utilitymonitor 110 optionally communicates with utility providers 190.

In some configurations, the utility meters 120, 130, 140 are directlyconnected to the user interface 150, or user computer 160, such asthrough a wired connection, including standard communication protocolsand adapters such as RS-232, Ethernet, serial, parallel port, SPI, SCSI,I2C, ZigBee, and USB connections. In a particular example, the utilitymeters 120, 130, 140 send signals to the user interface 150 over powerlines, such as using a power line modem. In a more particular example,the components of the system 100 communicate using the Homeplugcommunication standard. In further implementations, the utility meters120, 130, 140 generate wireless signals that are receivable by areceiver (not shown) of the user interface 150 or the user computer 160.In some implementations, the user computer 160 accesses the userinterface 150 through a web browser. For example, the user interface 150may be assigned an internet protocol (IP) address. In particularexamples, the user interface 150 communicates with the user computer160, remote system 170, or network 180 using the TCP/IP protocol.

In particular embodiments, such as the system 200 illustrated in FIG. 2,the utility monitor 110 is connected to the utility meters 120, 130, 140through respective adapters 182, 184, 186. In particular examples, theadapters 182, 184, 186 are designed to facilitate installation of themonitoring system 100.

In some aspects, the adapters 182, 184, 186 are designed to be installedby a consumer or other end user. In other aspects, one or more of theadapters 182, 184, 186 are designed to be installed by a professional,such as an electrician or plumber. The adapters (or sensors) 182, 184,186 may be powered by the local electrical supply (not shown), or mayhave a portable power supply (not shown), such as a battery. Inparticular examples, the portable power supply includes a solar cell,which may be used to power the adapter 182, 184, 186 or charge itsbatteries. Suitable adapters 182, 184, 186 are the Meter Interface Units(MIUs) available from Archnet of ShenZhen, China. In someimplementations, the electrical adapter 184 or electricity meter 130includes an in line shunt resistor, a current transducer, or a HallEffect sensor. Suitable Hall Effect sensors are available from GMWAssociates of San Carlos, Calif., such as the Sentron CSA-1V.

In particular implementations, the adapters 182, 184, 186, such as theadapter 182 for the water meter 120, include a photo sensor, such as aninfrared or optical sensor, that detects rotation of a dial mechanism.In one example, the sensor detects reflection of light off of the dialmechanism.

A light source, such as an optical or infrared LED, is included, incertain embodiments, to generate a signal to be measured. An integratedlight emitting diode and photodiode is available from Honeywell (PN#HOA1180). A marker, such as a piece of more highly light absorbing orreflecting material, may be placed on the dial in order to help trackrotation of the dial.

In further examples, a separate meter, such as a flow meter, isinstalled in the gas line or water line. A separate meter may also beincluded on the electrical line, such as a voltage or current meter. Inparticular implementations, the electrical adapter 184 is installedbetween an electrical socket and an existing electrical meter, such asan electrical meter installed by a power company. Suitable socketadapters 184 are available from RIOTronics, Inc. of Englewood, Colo. Insome implementations, the adapters 182, 184, 186 read signals, such aswireless signals, generated by an existing meter, such as a meterinstalled by a utility company.

In some implementations, the electrical adapter 184, or multipleelectrical adapters 184, is connected to one or more individual circuitsentering a measurement site. Each circuit may have a separate adapter184, such as an electric metering device, or multiple circuits may beindividually monitored by a single electrical adapter 184. In particularexamples, the electrical adapter 184 includes a current transducer (notshown) attached to the wires corresponding to each breaker switch in acircuit box. A multi-channel analog to digital voltage sensor (notshown) may be in communication with the current transducer tosimultaneously monitor multiple circuits.

The user interface 150 collects data from utility monitor 110. In someimplementations, the user interface 150 contains a processor andsoftware or firmware for processing the collected data. The data orprocessed data can be transferred from the user interface 150 to theuser computer 160. In particular examples, the user interface 150includes a display and user input device. The user can, in someexamples, be presented with information or control the system 100 usingthe user interface device 150. In some implementations, the userinterface is a device running the PALM operating system (available fromPalm, Inc., of Sunnyvale, Calif.), Embedded Linux (available fromuClinux at www.uclinux.org) or the Windows Mobile (Pocket PC) operatingsystem (available from Microsoft, Inc., of Redmond, Wash.).

In embodiments where the user interface 150 transfers collected data tothe user computer 160, or the utility meters 120, 130, 140 are directlyconnected to the user computer 160, suitable data processing software isinstalled on the user computer 160. In yet further implementations, theuser interface 150, or user computer 160, is in communication with theremote server 170. The remote server 170 processes the collected dataand displays summary information, reports, or other information to auser on the user interface 150 or user computer 160.

The consumption of a utility by a load, such as the use of an appliance,is associated with the consumption of one or more utilities, such as oneor more of water, electricity, gas, propane, coal, hydrogen, and heatingoil consumption. In embodiments where electricity adapters 184, such ascurrent transducers, are mounted on individual breaker switches, theoverall system 200 has additional information to associate with anelectrical load. Data from one or more meters 120, 130, 140, adapters182, 184, 186, or ambient sensors 145 may be used to identify aparticular utility load.

FIG. 3 illustrates another architecture for a utility monitoring system300. The monitoring system 300 includes an electricity meter adapter 310installed at each circuit 318 in a circuit breaker panel 314. Inparticular implementations, each adapter 310 monitors multipleindividual circuits 318. The monitoring system 300 also includes a gasmeter 320. The gas meter 320 may be implemented as described inconjunction with systems 100 or 200. In further embodiments, the gasmeter 320 is omitted or additional meters, such as water or heating oilmeters, are included in the system 300.

The gas meter 320 and electricity adapters 310 are in communication witha controller 330. The controller 330 includes a multiplexer 332, ananalog to digital converter and digital signal processor 334, and apower line interface device 336, such as a power line modem. Thecontroller 330 is in communication with a power line network interfacedevice 340 which, in turn, is in communication with a user computer 350.Powerline network integrated circuits are available from Intellon(PN#INT5200) of Ocala, Fla.

In particular examples, the controller 330 is a Blackfin Digital SignalProcessor (DSP), available from Analog Devices, Inc., of Norwood, Mass.This particular DSP has built in network connectivity and an SPIinterface (serial peripheral interface). In particular implementations,this controller 330 is programmed using DSP++ (Analog Devices, Inc.) orLabView (available from National Instruments, Inc., of Austin, Tex.).The analog to digital converter 334 may be a 16-bit converter connectedvia the SPI. The multiplexer 332 may, in particular examples, acquiredata from up to 32 channels.

Load Identification

Various types of information contained in the collected data can be usedto identify a particular load. For example measurements can be made of:the time the consumption began; the duration of the consumption; therate of consumption; the total amount of utility consumed during aparticular period; the maximum or peak use; the shape and magnitude ofthe electrical power waveform (such as the 60 Hz waveform); and anychanges in the rate of consumption. These measurements can be comparedto a library of standard values for different types of loads. Themeasurements can also be compared to a library of appliances previouslyobserved on the utility signal. As an example of how consumption can beused to identify a load, a toilet flush can be distinguished from ashower based on the duration of the consumption, the total amount ofwater consumed, and the water flow rate.

Time of day information can sometimes assist in identifying a load. Forexample water usage in the middle of the night is more likely to be dueto a toilet flush than a shower. Repetitive periodic water usagethroughout the day might be due to a sprinkler system whereas repetitiveperiodic water usage thought the day and night might be due to an icemaker. Even if the consumption patterns are not sufficient to completelyidentify the loads, they can still be used to help select the mostlikely candidates. The user can assist the appliance identificationprogram by linking an unidentified appliance to the name of an appliancethat is known have been in operation.

When the data is electrical data, additional information may be measuredand used to identify a load. For example, the shape and size of the 60Hz conductance waveform (defined as the current divided by the voltage)may be used to help identify the load. Typical resistive appliances,such as incandescent lights and clothes irons, draw current that is inphase with the AC voltage. Appliances with a reactive and resistive load(such as a DC transformer for a stereo amplifier and a motor on aclothes washer) draw current that is out of phase with the voltage. Yetother appliances, such as computers, have switching power supplies thatconsume power for brief intervals during a voltage cycle. Analysis ofthe amplitude and temporal variation of the current and power waveformscan help identify specific loads connected to a circuit. The circuit canbe characterized by its voltage and current measured at a particularsampling rate, such as 3840 Hz to provide 64 samples per voltage cycle.

For some loads, the current or voltage may be very stable. For example,certain light bulbs are either on or off. Other loads may operate atdiscrete values, such as a ceiling fan with 3 speeds. Further types ofloads will have a range of settings, such as a power drill havingvariable speed control. Finally, other loads (such as a refrigerator,TV, or computer) may have more complex combinations of conductance overtime.

Signal processing techniques can be used to disaggregate a bulk signal,such as the total household electrical or water use data, into itscomponent loads based on the unique properties of each load. A libraryof properties of common loads can be maintained and accessed by the userinterface 150, user computer 160, or remote system 170. For example, thelibrary can include properties of appliances from model years that aremost likely to be used in the monitored environment.

When located on the user interface 150 or user computer 160, thislibrary can be updated periodically, such as through the internet 180 bythe remote server 170. Other programming of the user interface 150, orsoftware running on the user computer 160, can also be updated via theinternet, such as with improved algorithms, heuristics, and the like. Incertain implementations, training or other user provided data is used toupdate a library that can be shared with other users. With a broad setof load profiles, the systems 100, 200, 300 will be able to, inparticular examples, automatically identify the loads consuming at leastabout 90% of the utilities in the monitored area.

In some aspects, the systems 100, 200, 300 use a processing algorithmthat employs statistical analysis, such as a least squares fit, toidentify individual loads. In a specific example, an effective varianceanalysis is performed on changes in conductance. Conductance is a usefulparameter to characterize the power consumption behavior of an appliancesince it is: (1) voltage independent (i.e. an appliance's conductancechanges minimally with normal fluctuations in voltage delivered to thecircuit) and (2) is additive for the calculation of power (i.e. theconductance on a circuit is the sum of the conductances of allappliances).

In some examples, the voltage and current waveform is sampled at asufficient rate such that many data points are collected for eachvoltage period. When the AC voltage V passes from negative to positive,current I and voltage V data points are each inserted into the firstcolumns of a two dimensional array. The number of rows in the array isdefined by the number of samples taken during a voltage cycle. When theAC voltage V passes from negative to positive again, the current andvoltage data are inserted into the next columns of the arrays and soforth. With this data, instantaneous values of the Power P (I*V)measured in Watts and Conductance G (I/V) measured in Siemens can becalculated. In particular examples, the average and standard deviationof the rows of the conductance array is calculated on a one secondbasis.

The current and voltage waveforms are measured as:

I=I₁,I₂, . . . I_(n)

V=V₁,V₂, . . . V_(n)

Where n is the number of samples measured during each voltage cycle.Similarly, the conductance of each sample is also calculated:

${G = \frac{I_{1}}{V_{1}}},\frac{I_{2}}{V_{2}},{\ldots \mspace{14mu} \frac{I_{n}}{V_{n}}}$

The average and standard deviation of each element of I, V, and G iscalculated for each non-transient (non-event) period by averaging overall voltage cycle waveforms over a period for example 1 second. Tominimize memory needs for the system, the point-by-point calculationsare used to calculate the mean and standard deviations. This isaccomplished by tracking the number of measurements, the sum of thesample measurements, and the sum of squares of the sample measurements.The mean is calculated by the equation:

$\mu_{m} = \frac{x_{m} + {\sum\limits_{j = 1}^{m - 1}x_{i}}}{m}$

The standard deviation is subsequently calculated using the followingequation:

$\sigma_{m} = \sqrt{\frac{x_{m}^{2} + {\sum\limits_{j = 1}^{m - 1}x_{i}^{2}}}{m} - \mu_{m}^{2}}$

In this case, the variable x is the waveform of conductance over thej^(th) voltage cycle. An example of conductance waveforms for fourappliances is shown in FIG. 4. In this example, the data were collectedwith a 13-bit digital to analog converter with an electrical currentsensitivity of 16 mA per bit. The Figure shows substantial variation ofthe conductance waveforms for the different appliances.

The states of appliances on a particular circuit are identified byapplying an effective variance calculation using all combinations ofappliance waveforms known to exist on that particular circuit.

The effective variance method seeks to find the minimum of the followingequation:

$\chi^{2} = {\sum\limits_{i = 1}^{n}\frac{\left( {G_{i} - {\sum\limits_{k = 1}^{p}{a_{i,k}S_{k}}}} \right)^{2}}{\sigma_{G_{i}}^{2} + {\sum\limits_{k = 1}^{p}{\sigma_{a_{i,k}}^{2}S_{k}^{2}}}}}$

Where X² is the cost function to minimize, i is the element within avoltage cycle period, n is the number of elements within one period, kis a particular appliances out of a total of p known appliances withinthe appliance library, a_(i,k) is the i^(th) element of the conductancearray of appliance k, S_(k) is the binary state of appliance k (1=on,0=off), and σ is the standard deviation of the i^(th) element of eitherG or a.

A transition event is defined to begin when the running X² (comparingthe interval from one period to the same sized interval in the nextperiod) exceeds a certain threshold, indicating a change in theoperational state of one or more appliances connected to the circuit.The transition event is defined to end when the running X² falls below apredetermined threshold. Typical applications use an interval of 1second for the running X² calculation. All combinations of S_(k) aretested to find the minimum X². If the minimum X² is above a certainthreshold, a new device is added to the appliance library.

For a total of p appliances, there are 2^(p) different combinations ofS. Since devices are generally turned on one at a time, S usuallychanges only one element at a time and the search for the optimal S canbegin with p different combinations of S thereby reducing the number ofcalculations needed to find S. When looking at power increases, thenumber of combinations to be analyzed can be further reduced by omittingdevices that are already on as possible candidates. Similarly, whenlooking at power reductions, devices that were not previously on can beomitted in determining possible candidates. In these situations, thenumber of combinations is less than p.

For appliances that are continuously variable (i.e. a light with adimmer), the different states will initially be identified as differentappliances. As the user trains the appliance identification algorithm,the algorithm will identify the appliance based on the interpolation ofalready measured appliance states.

The disaggregation algorithm is configured, in some embodiments, toidentify small loads that are turned on while a much larger load isactive. Failure to achieve this separation can cause the loadidentification techniques to inaccurately group various appliances andproduce erroneous results. The following example demonstrates theability of the effective variance technique to determine the operationalstate of an 18 W reading lamp while a 2100 W air conditioner is turnedon.

A comparison of the conductance profiles are shown in FIG. 5. The errorbars in the chart indicate that the variability of the conductancesamples of the combination of both reading lamp plus air conditionerspans the conductance samples of the air conditioner alone.

A stochastic analysis was performed by simulating waveforms assumingeach I^(th) measurement of conductance is normally distributed about itsmean. A random number generator was used to add error to each averagevalue to simulate the variation of repeated measurements. Theuncertainty of the average sample value (i.e. the standard error)decreases as the number of samples increases and is defined as thestandard deviation divided by the square root of the number of samples.Based on this relationship, the average conductance waveforms for avariety of averaging periods ranging from 0.0167 seconds (one voltagecycle) up to 30 seconds were simulated.

The X² was calculated for each sample point in the waveforms bycomparing one simulation of the air conditioner conductance (i.e. thetest set) with another simulation of the air conditioner conductance(“case one”). The standard deviations of each conductance measurementwere used in calculating the X². This calculation was repeated for theconductance waveform of the test set comparing it with the conductanceof the air conditioner plus the reading lamp (“case two”).

The top panel of FIG. 6 shows the X² values calculated for only onevoltage cycle. It can be seen that these are indistinguishable,indicating that when averaging for one voltage period (0.0167 seconds),the operational state of the lamp can not be determined when the airconditioner is on. As averaging periods increase the average X² timesthe number of waveforms sampled increases for case two, whereas thevalues for case one remain constant.

FIG. 7 shows a comparison of the average X² values times the number ofvoltage cycles for a range of averaging periods. The load identificationalgorithm selects the combination of appliances with the lowestchi-square. This example shows that the operational state of the lampcan be determined while the air conditioning is on after approximately 1second. This is a substantial improvement in appliance selectivity overexisting methods that use only real and reactive power (and occasionallyodd number harmonic components) to infer the operational state ofappliances. Accordingly, the averaging period can be chosen to provide adesired level of load resolution.

The disclosed methods can also yield other useful information for powerquality assurance such as THD (total harmonic distortion Eq. 4) and PF(power factor Eq. 5) which are useful for electric utilities and moreinformed users:

$\begin{matrix}{{T\; H\; D\mspace{14mu} (\%)} = {100*\frac{I_{n = 1}}{\sum\limits_{2}^{\infty}I_{n}}\mspace{14mu} {\%.}}} & {{Eq}.\mspace{14mu} 4} \\{{P\; F} = \frac{\sum\limits_{n = 1}^{\infty}{V_{n}*I_{n}*{\cos \left( {\phi_{n} - \varphi_{n}} \right)}}}{\sum\limits_{n = 1}^{\infty}{V_{n}*I_{n}}}} & {{Eq}.\mspace{14mu} 5}\end{matrix}$

where n is the index of the fundamental frequency of the current I andvoltage V. The term (φ−ø) is the difference in phase angles between thecurrent and voltage.

The THD and PF are typically monitored by electric utilities, and largepower consumers often face penalties for violating establishedstandards. The disclosed systems can be used to monitor these quantitiesand determine which loads have a detrimental effect on the powerquality. Examples of data processing techniques that can be used ormodified for use in electrical load analysis in the disclosed systemsand methods are disclosed in Hart, “Nonintrusive Appliance LoadMonitoring,” Proceedings of the IEEE 80(12), 1870-1891 (December 1992),and U.S. Pat. No. 5,717,325, each of which is expressly incorporated byreference herein in its entirety (in the case of any inconsistency withthe present disclosure, the present disclosure shall control).

In particular embodiments, the disaggregation algorithm uses timesequence heuristics based on likely or historical load use. Appliancesare often used in a particular order. For example, a user's morningroutine may be to take a shower, blow dry their hair, start a coffeemaker, and make toast. In one aspect, the time a particular loadconsumes a utility can be used to help identify the load. For example,the aforementioned activities may typically occur in the morning.Accordingly, when discriminating between two possible loads to assign toa particular source of consumption, the time the load was activated mayindicate assigning one load over another. Similarly, a particularsequence of loads may be indicated as more probable than anothersequence. For example, “shower”-“hair dryer”-“coffee maker” may beindicated as more likely than “washing machine”-“hair dryer”-“coffeemaker”. Both time-based and sequence-based heuristics may be weightedsuch that heuristics that are more probably true are weighted moreheavily than heuristics that are less probable.

In further embodiments, the disaggregation algorithm uses a combinationof data from two or more utilities to identify a load. For example,certain appliances, such as a dishwasher, use multiple utilities.Appliances that use a combination of utilities can thus be distinguishedfrom appliances that use fewer utilities. Analyzing data from multipleutilities can also be used to identify loads, even when the load onlyconsumes one utility.

For example, FIG. 8 presents simulated utility data simultaneouslyobtained from a gas meter, an electric meter, and a water meter. Duringthe observed time, the user flushed a toilet, ran a washing machine, andtook a bath or a shower. In addition, the user's furnace was activated.

Looking at the water trace, area 406 represents the toilet flushing.Areas 416, 418, 420 represent wash and multiple rinse cycles of thewashing machine. Area 426 represents the user's bath or shower. Thetoilet flush 406 can be distinguished from the shower 426 and washingmachine cycles 416, 418, 420 based on the duration of the utility useand the pattern of use. For example, the washing machine pattern 416,418, 420 may be a standard water use pattern for the particular washingmachine model owned by the user. The toilet flush 406 may be identifiedbecause it uses a fixed amount of water per flush.

Areas 430, 432, 434 of the electricity trace represent wash and rinsemotor cycles of the washing machine. Again, the areas 430, 432, 434 maybe characteristic of the particular washing machine used. Theidentification of areas 416, 418, 420 and 430, 432, 434 as being due tothe washing machine is further confirmed by the relation of water use toelectricity use. The washing machine would be expected to use bothelectricity and water. Furthermore, the water and electricity use show aclear pattern of the washing machine filling with water, followed byactivation of the washing machine motor.

The electricity trace also shows a longer, less intense period ofelectricity use at area 440. The area 440 may correspond to lightingused during the user's shower.

The gas trace shows heater use at areas 450, 452. Areas 450, 452 can beassigned to the heater based on the duration and magnitude of the heateruse, as heating likely uses a consistent gas draw rate. Furthermore,areas 450, 452 do not correspond to any electricity or waterconsumption, further confirming that the gas use is due to a heater. Gasuse area 460 is assigned to a gas hot water heater. This assignment canbe based on the degree and duration of gas use, as well as thecorrelation with hot water use during the user's shower, as confirmed bythe electricity use area 440 and the water use area 426.

FIG. 9 presents electricity and water traces for simulated utility use.The water trace contains an area 510 associated with a morning shower.An area 520 represents a sink running and an area 530 representsactivation of an automatic sprinkler system. The electricity traceexhibits an area 540 associated with the use of a hairdryer and an area550 representing activation of a toaster.

The identification of the utility loads can made based on the time ofday of the consumption, the correlation of different utilityconsumption, and sequence heuristics. For example, the relatively long,steady water consumption of area 510 is closely followed in time by theshort, high electricity consumption area 540. The combination of thesetwo events, as well as the time of day, helps confirm the assignment ofthese areas to a shower and hair dryer.

Similarly, without any other information, the area 550 might bedifficult to assign to a toaster versus a number of other small wattageappliances. However, the time of day, occurrence after the shower 510,and before the running of the sink at area 520, helps establish area 550as being due to a toaster as part of the user's morning routine. If thetime of day indicated that the sun was up, this would indicate that area550 was unlikely to be a lighting device. In case of conflict, such asthe time of day indicating that it was dark outside, the multiple datastreams can be compared and the most probable load chosen based on theheuristics most likely to produce the correct result. In case of error,the user can correct the system and help train the system to be moreaccurate in future predictions. The results can be presented with aparticular confidence level to give the user an idea of the likelyaccuracy of the data.

For example, in certain implementations, a user can program the userinterface 150, such as by adding or editing library entries or bytraining the user interface 150 with user input. For example, thesystems 100, 200, 300 can include a handheld unit, such as a PALM basedor Window Mobiles based computer. As an example, during a trainingsession, the handheld unit displays a list of devices within themonitored area and the user indicates if the devices are on or off. Infurther embodiments, the handheld unit may specifically direct a user toturn a load on or off. With sufficient training, the systems 100, 200,300 can identify when most of the major devices are on and how much of aparticular utility each device is consuming. The user can then associateparticular loads, behaviors, settings, and activities with specificcosts.

FIG. 10 presents a dataflow diagram of a process 600 of using thedisclosed utility monitoring systems, such as the monitoring systems100, 200, 300. Gas use is measured at a plurality of times at steps 608,610, 612. Electricity use is measured at a plurality of times at steps618, 620, 622. Water use is measured at a plurality of times at steps628, 630, 632. Of course, the gas, electricity, and water use may becontinuously monitored, with times 608, 610, 612, 618, 620, 622, 628,630, 632 representing particular data points.

The acquired data is transmitted to, and stored in, a storage device inprocess 640. The storage device may be, for example, the user interfacedevice 150, the user computer 160, or the remote server 170. The data isthen processed, sequentially or in parallel, using various data analysisor disaggregation processes. For example, the rate of utility use, totalamount of utility consumed, time of consumption, duration ofconsumption, and degree of consumption (such as the highest amount ofthe utility drawn) are analyzed in process 644. Process 648 analyzes thedata using sequence based heuristics, as previously described. Process652 analyzes the data using correlation based heuristics, as previouslydescribed. For example, process 652 may correlate water use toelectricity or gas use. Process 656 may apply phase lag heuristics oranalysis to the data. The above-described effective variance analysis ofthe conductance may be applied in process 658. The processes 644, 648,652, 656, 658 may be applied multiple times, such as in an iterativeprocess, in order to disaggregate the acquired data into utility useinformation for discrete loads in a monitored area.

A process 660 receives the results of the processes 644, 648, 652, 656,658. In particular implementations, the process 660 applies furtherheuristics, or hierarchical rules regarding the processes 644, 648, 652,656, 658 to select the most likely sources of utility use giving rise tothe observed data. In further implementations, the process 660 consultsa database 664 of utility loads in order to help identify discreteloads. The process 660 may send data back to one or more of theprocesses 644, 648, 652, 656, 658 for additional analysis.

Once the observed data has been disaggregated into utility consumptionfrom discrete loads, the information may be presented to a user atprocess 670. For example, the information may be presented on the userinterface 150 or user computer 160.

Methods of Using Utility Monitoring Systems

When the disclosed systems include water monitoring, the systems can beused to detect water leaks. For example, the system can be used tolocate a leaky faucet, toilet, or sprinkler. In particularimplementations, water consumption exceeding a certain duration, orexceeding a certain duration and particular rate, may indicate a leak orsimilar problem. In some implementations, the system provides anotification to the user, such as an alert when the user accesses thesystem, an email, page, or telephone call. In further implementations,the system may shut off the water, such as to prevent damage to themonitored area, or may automatically call a service provider. Similarmeasures may be implemented with other utilities, such as gas orelectricity.

The system can also be in communication with, or otherwise monitored by,a utility company. In such implementations, the system can be used toenforce restrictions, such as watering restrictions. The system canprevent unauthorized use, or restrictions can be enforced, such as byissuing a fine, after data is reviewed by the utility. Similar measurescan be implemented for gas or other utilities.

In certain aspects of the present disclosure, it is undesirable,unnecessary, or overly complicated to identify numerous individual smallloads. Accordingly, loads can be grouped into a particular category. Forexample, rather than measuring the electricity use of multipleincandescent or fluorescent light bulbs, data can be collected orcalculated for all such similar loads. Abnormally low or high sub-loadscan generate a flag or alarm to indicate to the user that there may bean issue with a particular load category.

The user computer 160 receives data from the user interface 150 and canpresent a user with various reports. Such reports may be presented onthe user interface 150, in particular examples. Such reports mayinclude, for example, utility consumption summaries for a load, loadcategory, or group of loads, such as a circuit. The consumption summarymay be for a particular time period, such as a day, week, month, year,or period of years. Consumption over various periods of time can be usedto track increased utility consumption over a time period. In furtherimplementations, the user reports are provided to the user computer 160over the network 180, such as from the remote server 170. In someconfigurations, the user interface device 150 provides the reports tothe user, and may be in communication with the network 180 or remoteserver 170.

Consumption for a particular load can be compared, or benchmarked,against standard values. For example, a particular appliance may beassociated with a standard data pattern. Comparison of the measured datawith the standard data may be used to indicate the condition of theuser's appliance, such as whether the appliance needs maintenance orshould be replaced. The comparison may be used to help identify the typeof maintenance or repair needed. For example, increased fan effort overtime may indicate a clogged air filter in a furnace or air conditioningunit.

In further aspects, the user's consumption data is compared to similarusers, such as users in the same zipcode, who have similar loads, or whohave similar monitored areas. Such a comparison may allow a user todetermine if they are consuming more utilities, or producing morepollution, than other similarly situated users. Such data can enablechanges in user behavior, such as consuming less utilities or installingmore efficient loads.

FIG. 11 illustrates a embodiment of a user interface screen 700 that canbe displayed on the user interface device 150 or user computer 160. Thescreen 700 presents the total cost of consumed utilities 710 and theabatement cost 720 associated with pollution caused by the utilityconsumption 710 and estimated green house gas emissions 725. A picture730 of the monitored area is presented and the consumed utilities 710are broken down by type, such as lighting 740, appliances 742, and gas744. The user can get more information on each of these utilitysubcomponents, such as a detailed report for each associated load, byactivating an appropriate link 750.

The screen 700 also presents climate control information 760, includingheating costs 762, cooling costs 764, the temperature 766 outside themonitored area, and the temperature 768 within the monitored area. Thescreen 700 also rates the efficiency of the climate control system, suchas using a star rating system 770. A user may choose to see moredetailed information regarding each climate control component byselecting an appropriate link 772.

In another aspect, the data from the system may be used to benchmark theefficiency of the heating, ventilation, air conditioning, and insulationof a building, collectively referred to as the “thermal efficiency.”When configured to measure electric power and natural gas (or heatingoil) consumption, the device can attribute specific costs to the HVACsystem. Users can benchmark their building's thermal efficiency bycalculating a U factor based on the following equations:

$U_{Heating} = \frac{{{Heating}\mspace{14mu} {Energy}} + {{Ventilation}\mspace{14mu} {Energy}}}{\begin{matrix}{{Building}\mspace{14mu} {{Area} \cdot}} \\\left( {{{Inside}\mspace{14mu} {Temperature}} - {{Outside}\mspace{14mu} {Temperature}}} \right)\end{matrix}}$$U_{Cooling} = \frac{{{Cooling}\mspace{14mu} {Energy}} + {{Ventilation}\mspace{14mu} {Energy}}}{\begin{matrix}{{Building}\mspace{14mu} {{Area} \cdot}} \\\left( {{{Outside}\mspace{14mu} {Temperature}} - {{Inside}\mspace{14mu} {Temperature}}} \right)\end{matrix}}$

This feature is useful for comparing the efficiencies of differentbuildings and deciding how to invest in HVAC and/or insulation upgrades.Such information can affect building purchasing decisions.

In a further aspect, the consumption for a particular load can be usedto help the user determine whether the user should purchase a newappliance. For example, based on use history, as well as projectionsbased on such historical consumption, the projected cost of a newappliance can be calculated and compared to the projected cost ofcontinuing to use the existing appliance. If a significant amount ofutility consumption can be avoided, the user may decide to purchase anewer or more efficient appliance. In particular examples, the systems100, 200, 300 are used to determine whether, for example, a gas orelectric appliance, such as a water heater, oven, range, clothes drying,or furnace, will be more cost effective.

The systems 100, 200, 300 may also be used to help a user to decidewhether to take steps to make the monitored area more energy efficient.In particular examples, the systems 100, 200, 300 can estimate theenergy savings that may be achieved through various measures, such asthe installation of insulation, installation of energy efficientwindows, or repair of leaky air ducts.

The disclosed systems, particularly those linked to a remote server 170,may be used to present the user with offers from merchants or servicetechnicians. For example, a company may advertise a new, more efficientrefrigerator to a user through the system. In particular examples, theuser's data is shared with such merchants and technicians so that theadvertisements or solicitations are tailored to the user. For example, auser whose data indicates an appliance is in need of repair may receiveadvertisements from a service technician in the user's area. Inparticular aspects, the user may choose to subscribe to a service planand a service technician may automatically be dispatched when the systemindicates a load is in need of repair. In particular examples, a usercan choose whether to receive such solicitation, or whether to sharetheir data.

In certain aspects, the systems 100, 200, 300 are used to control or setload use based on the user's cost preferences. For example, the system100 can provide a thermostat setting that will yield a particularmonthly energy cost. The user may also be presented with a range oftemperature options and their associated costs. The user may manuallyset or program the thermostat. In further examples, the systems 100,200, 300 are in communication with the thermostat and automaticallycontrol the thermostat in accordance with a user's energy or costpreferences.

In some implementations, the thermostat is capable of variably heatingor cooling different sub-areas of a monitored site, such as differentrooms in a house or hotel. In one example, a user can manually setdifferent heating or cooling programs for the sub-areas. In anotherexample, the systems 100, 200, 300 automatically adjust utility usebased on programmed conditions. For example, an absence of utilityconsumption in a particular room may indicate that it is unoccupied.Accordingly, the systems 100, 200, 300 may adjust the heating or coolingto that room to use less energy. If utility consumption subsequentlyindicates the room is occupied, the systems 100, 200, 300 appropriatelyadjust the heating or cooling to the room.

The systems 100, 200, 300, in further embodiments, function as asecurity system. Unexpected patterns of utility consumption can indicateenergy siphoning or unauthorized access from or to the monitored area.For example, a user can indicate that he or she is going on vacation sothat the system 100, 200, 300 expects minimal energy consumption. Insome examples, energy consumption above the minimal level triggers analarm or notifies law enforcement authorities, or a security company.

In certain aspects, the present disclosure is used to schedule utilityuse based on variable rates. For example, a utility may be priced lowerat off-peak hours. A user may program the system to activate or changeutility consumption based on such rate data. In a particularimplementation, the system automatically switches utility consumption,when possible or according to a program, such as a user defined program,to off-peak times.

Some embodiments of the present disclosure allow a user to monitor, andattempt to mitigate, their utility consumption and its associatedenvironmental consequences. For example, the system may display both theactual utility cost of the user's behavior and the predictedenvironmental cost of their behavior. For example, the user may bepresented with the monetary cost required to abate the environmentaldamage associated with their utility consumption. Even if the user isnot concerned by the cost of their utility consumption, they may wish tomitigate environmental damage. The system may present the user withoptions to both reduce their utility consumption and ways to reduce theenvironmental consequences of their actions. In certain situations, auser may wish to pay more for some utilities in order to reduce theenvironmental consequences of their actions.

Although the present disclosure generally describes apparatus, systems,and methods for monitoring utility consumption, the present disclosuremay also be applied to utility generation. For example, a monitored areamay receive supplemental electrical power from solar cells. The systems100, 200, 300 are, in certain implementations, configured to regulatepower drawn from the supplemental source and the main source, such asdrawing power from the main source to make up for any shortfall in theamount generated by the supplemental source. In further implementations,the systems 100, 200, 300 measure an amount of power generated by thesupplemental source and transferred to a main power grid, as a user mayreceive payment for such generated power.

The systems and methods of the present disclosure can provide a numberof advantages. One such advantage is that utility consumptioninformation can be immediately provided to a user. Such a configurationcan be advantageous compared to other systems which periodically uploaddata to a remote data collection site for processing. In particularembodiments, presently disclosed systems continuously provide data to auser interface, allowing the user to immediately view current utilityconsumption data. Because aspects of the present disclosure allow a userto track utility consumption associated with particular loads, such asappliances, the user can make informed decisions about their utilityconsumption.

It is to be understood that the above discussion provides a detaileddescription of various embodiments. The above descriptions will enablethose skilled in the art to make many departures from the particularexamples described above to provide apparatuses constructed inaccordance with the present disclosure. The embodiments areillustrative, and not intended to limit the scope of the presentdisclosure. The scope of the present disclosure is rather to bedetermined by the scope of the claims as issued and equivalents thereto.

1-28. (canceled)
 29. A utility monitoring method comprising: acquiringelectricity consumption data from each of a plurality of circuits;transmitting the electricity consumption data to a user interface deviceor user computer; applying a disaggregation algorithm to the electricityconsumption data to identify individual loads of electricity consumptionin the electricity consumption data; and presenting a user withelectricity consumption data for each of the individual loads.
 30. Themethod of claim 29 wherein applying a disaggregation algorithm to theelectricity consumption data comprises: obtaining voltage and currentdata from a plurality of electrical devices; analyzing conductancewaveforms of the voltage and current data; comparing the conductancewaveforms to a library of waveforms; and identifying the plurality ofelectrical devices based on the comparison of the conductance waveformsand the library of waveforms.
 31. The method of claim 30 whereincomparing the conductance waveforms to a library of waveforms comprisesperforming an effective variance analysis.
 32. The method of claim 30,further comprising averaging the conductance waveforms prior tocomparing the conductance waveforms to a library of waveforms.
 33. Themethod of claim 29 further comprising determining power statetransitions of at least one of the plurality of electrical devices basedon its conductance waveform.
 34. The utility monitoring method of claim29, wherein applying a disaggregation algorithm to the electricityconsumption data comprises: measuring an amount of electricity consumedby an individual load; measuring an amount of another utility consumedby a device associated with the individual load; and based on themeasured electricity amount and measured another utility amount,determining the identity of the device.
 35. The utility monitoringmethod of claim 29, wherein applying a disaggregation algorithm to theelectricity consumption data comprises: measuring an amount ofelectricity consumed by a first device at a first time; measuring anamount of electricity consumed by a second device at a second time; anddetermining the identity of the first device based on the measuredelectricity consumption of the first and second devices at the first andsecond time.
 36. The utility monitoring method of claim 29, whereinapplying a disaggregation algorithm to the electricity consumption datacomprises: determining an amount of electricity consumed by a firstdevice at a first time; measuring the amount of another utility consumedby the first device at a second time, which may be the same as the firsttime; determining an amount of electricity consumed by a second deviceat a third time, which may be the same as the first or second time;measuring an amount of another utility consumed by the second device ata fourth time, which may be the same as the first, second, or thirdtime; and determining the identity of the first device based on theamount of electricity and another utility consumed by the first andsecond devices at the first, second, third, and fourth times.
 37. Theutility monitoring method of claim 29, wherein presenting a user withelectricity consumption data for each of the individual loads occurs atleast substantially at the same time as acquiring electricityconsumption data.
 38. The method of claim 29 further comprisingproviding an at least substantially real time estimate of greenhouse gasemissions due to an electrical device associated with at least one ofthe individual loads.
 39. The method of claim 29, further comprisingdetermining the thermal efficiency of a monitored area.
 40. A utilitymonitoring method comprising: measuring an amount of a first utilityconsumed by a load associated with a device; measuring an amount ofsecond utility consumed by a load associated with the device; and basedon the amount of the first and second utilities measured, determiningthe identity of the device.
 41. The utility monitoring method of claim40, wherein the first utility and the second utility are, independently,electricity, water, gas, oil, propane, coal, hydrogen, or solar power.42. The utility monitoring method of claim 40, wherein the first andsecond utilities are consumed at different times.
 43. A utilitymonitoring method comprising: measuring an amount of a utility consumedby a first device at a first time to determine a utility amount consumedby the first device; measuring an amount of a utility consumed by asecond device at a second time, which may be the same as the first time,to determine a utility amount consumed by the second device; anddetermining the identity of the first device based on the utilityamounts consumed by the first and second devices at the first and secondtimes.
 44. The utility monitoring method of claim 43, whereindetermining the identity of the first device based on the utilityamounts consumed by the first and second devices at the first and secondtimes, comprises: determining a first hypothetical consumption scenariocomprising the consumption of a first possible first device and a firstpossible second device; determining a second hypothetical consumptionscenario comprising the consumption of a second possible first deviceand a second possible second device; comparing the first and secondhypothetical consumption scenarios; selecting the first or secondhypothetical consumption scenario as the more probable scenario; andidentifying the first device based on the selected hypotheticalconsumption scenario.
 45. The utility monitoring method of claim 43,wherein the utility amount consumed by the first device is an amount ofa first utility consumed by the first device, further comprisingmeasuring an amount of second utility consumed by the first device. 46.The utility monitoring method of claim 43, wherein the utility amountconsumed by the second device is an amount of a first utility consumedby the second device, further comprising measuring an amount of a secondutility consumed by the second device.
 47. The utility monitoring methodof claim 43, further comprising measuring an amount of a utilityconsumed by the first device at a third time, which may be the same asthe second time if the second time is different than the first time.